Worksheets/Week2

A baked food product:

• T = time used to bake the product
• F = quantity of fat added

# T = time used for baking: (-1) corresponds to 80 minutes and (+1) corresponds to 100 minutes T <- c(-1, +1, -1, +1) # F = quantity of fat used: (-1) corresponds to 20 g and (+1) corresponds to 30 grams F <- c(-1, -1, +1, +1) # Response y is the crispiness y <- c(37, 57, 49, 53) # Fit a linear model model_crispy <- lm(y ~ T + F + T*F) summary(model_crispy) # Uncomment this line if you run the code in RStudio #library(pid) # Comment this line if you run this code in RStudio source('https://yint.org/contourPlot.R') # See how the two factors affect the response: contourPlot(model_crispy ) interaction.plot(T, F, y) interaction.plot(F, T, y) # Make a prediction with this model: xT = -1 # corresponds to 110 minutes xF = -1 # corresponds to 20 grams of fat y.hat <- predict(model_crispy, data.frame(T = xT, F = xF)) paste0('Predicted value is: ', y.hat, ' crispiness.')